Statistical methods by regression analysis or the like are widely used in quality management of a manufacturing process or the like as a technique of elucidating relationships between a response variable and explanatory variables and identifying an explanatory variable that strongly influences the value of the response variable.
For example, a factor identification method that identifies an explanatory time series that influences a change in value of a response time series is used in a production process to identify a sensor observation value that influences the results of quality tests and the like of manufactured goods. A majority of analysis methods, represented by regression analysis, are methods of multidimensionally analyzing observation data on the premise of availability of data that is observed by measurement instruments, such as sensors.
PTL 1 describes a method of identifying an influence factor by segmenting data based on nominal scale data when explanatory variables include the nominal scale data and using a multivariate analysis method for each segment.
PTL 2 describes a quality variation cause analysis method of a production line, which repeats operation of dividing a plurality of explanatory variables and narrowing down the explanatory variables by performing multiple linear regression analysis for all division groups.
NPL 1 describes a method, called L1 regularized logistic regression, which can estimate influence degrees of explanatory variables with high precision when a response variable is a discrete value.
NPL 2 describes a random forest classifier that is a classifier implemented using a plurality of decision trees. The techniques described in PTL 1 and 2 and NPL 1 and 2 are also used in factor analysis.